Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
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Big data
1.
2. Susan Etlinger
Susan Etlinger is an industry analyst with
Altimeter Group, where she focuses on data
and analytics.
She conducts independent research and has
authored two intriguing reports: âThe Social
Media ROI Cookbookâ and âA Framework for
Social Analytics.â
She also advises global clients on how to work
measurement into their organizational
structure and how to extract insights from
the social web which can lead to tangible
actions.
In addition, she works with technology
innovators to help them refine their roadmaps
and strategies.
3. What is BIG DATA?
â âBig Dataâ is similar to âSmall Dataâ, but bigger in size
â But having data bigger it requires different approaches:
-Techniques, tools and architecture
â An aim to solve new problems or old problems in a better way
â Big Data generates value from the storage and processing of very large
quantities of digital information that cannot be analyzed with traditional
computing techniques.
â Walmart handles more than 1 million customer transactions every hour.
â Facebook handles 40 billion photos from its user base.
â Decoding the human genome originally took 10 years to proces; now it can be
achieved in one week.
4. So what does this mean for Analytics?
So what does this
mean for Analytics?
Yes, the amount of data that is available to us is exploding
And Big Data Platforms and Commodity Hardware and bringing in additional capabilities
Media is rife with Big Data and Analytics
Big Data and analytics is touted as the panacea for all problems
âŠmakes it to on top of CIO agenda AND
The Data Scientist makes it from Nerd to the most cool person!!
.
8. Why Big Data?
â FB generates 10TB daily
â Twitter generates 7 TBof
Data Daily
â IBM claims 90% of
todayâs store data was
generated in just the last
two years
9. How is Big Data Different ?
1) Automatically generated by a machine
(e.g. Sensor embedded in an engine)
2) Typically an entirely new source of data
(e.g. Use of the internet)
3) Not designed to be friendly
(e.g. Text streams)
4) May not have much values
â Need to focus on the important part
10. Three Characteristics of Big Data V3S
1. Volume (Data Quantity)
Boeing 737 generate 240 terabytes of flight data during a single flight across the US
2. Volume (Data Speed)
Machine to machine processes exchange data between billions of devices
3. Variety (Data Types)
Big Data isnât numbers, dates and strings. It is also geospatial data, 3D data, Audio and Video and
Unstructured text, including log file and social media
11. The Structure of Big Data
â Structured
Most traditional data sources
â Semi-structured
Many sources of big data
â Unstructured
Video data, audio data
12. 3 new insights from the video
1
Big Data = Poor
Data
â The more data you
have, the less probable
your chance of
discovering meaning --
the "why" of things.
2
To accelerate our
demise
â "We are in an age where
guided missiles are
operated by misguided
men"
â Unless we slow down
and analyze our needs
we will surely
accelerate our demise
3
Coding is not in
our control
â Big data can be a
transformative force,
and should be treated
as such
13. Why and How these Insights relevant
to Managers in India ?
14. 1. Cost Reduction
Big data technologies such as Hadoop and cloud-based
analytics bring significant cost advantages when it comes
to storing large amounts of data â plus they can identify
more efficient ways of doing business.
15. 2. Faster, Better Decision Making
With the speed of Hadoop and in-memory analytics,
combined with the ability to analyze new sources of data,
businesses are able to analyze information immediately â
and make decisions based on what theyâve learned.
16. 3. New Products and Services
With the ability to gauge customer needs and satisfaction
through analytics comes the power to give customers
what they want. Davenport points out that with big data
analytics, more companies are creating new products to
meet customersâ needs.
19. Analytics is playing an ever important role
Increased Focus on identifying the
customer across all channels
Segmentation to Micro segmentation
to the individual
Personalized Messaging and offers â
Increased Individual Customer Centricity
Gradual evolution of Customer Analytics
Past
âȘ Customer segments who are
most likely to respond to
targeted campaigns for new
products offers
âȘ Can tailor offers to specific to
each customer segment
âȘ Mostly delivered through mass
mail campaigns and in store
promotions.
Now
âȘ Micro segmentation
âȘ Analyze customer behavior
and buying patterns across
channels
âȘ Delivery through email, web,
mass mail campaigns.
Moving toward
âȘ Historical individual customer behavior
and buying patterns across channels
âȘ Individual customer consumption
pattern
âȘ In-store basket analytics
âȘ Additional dimensions Location & time
âȘ Targeted Strategies to pre-empt
customers from visiting competition
âȘ Instantaneous Delivery in store or a
proactive delivery via mobile to bring the
customer to store.
Segment to Individual to Individual @ time, place and behavior
You have purchased
Cheese, here are
the
offers on Bagels
You are within 2
KMs of a store
offering 50% off
garden furniture
Do you
need coffee?
20. much of which is outside the
organization
Increased availability of data
Analytics as a Service and Data
Monetization
New service models
Decreasing Time value of data!
21. Scalability and industrialization to address skill shortage
Key to a Great Data Scientist
Technical skills (Coding, Statistics,
Math)
+ Perseverance
+Creativity
+ Intuition
+Presentation Skills
+Business Savvy
= Great Data Scientist!
âȘ Identified four Data Scientist clusters based on
how data scientists think about themselves and
their work, not
âą Years of experience,
âą Academic degrees, favorite tools
âą Titles, pay scales, org charts.
âȘ Most successful data scientists are those
with substantial, deep expertise in at
least one aspect of data science, be it
statistics, big data, or business
communication
âȘ T-Shaped Skills.